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Advice to New Grads for “Big Data” Jobs in Retail

There’s no shortage of coverage of late for “big data analytics” and the hype around it:  Forbes’ How Target Figured Out A Teen Girl Was Pregnant Before Her Father Did”  and The NYT’s “How Companies Learn Your Secrets”.   For the moment we’ll set aside whether this is new (we don’t think so), whether bigger data equals better data (nope), and whether the future will look very different than the present (not likely).  Instead we’ll focus on how new grads should best prepare for this burgeoning marketplace.

Our answers have surprised some because we don’t advise diving into the latest and greatest statistical or modeling technique.  Rather we advise students that the biggest gaps we see today are at either ends of the process: the first step in data, and the last step in putting it back together into a business case recommendation.

Data:  In business it’s rare to have your data cleaned, processed, aggregated, and integrated before it’s given to you, with emphasis on cleaned.  There’s no shortage of examples of ugly data, but an old favorite has to do with fiscal months.  A junior analyst put together sales trends by month for two very different categories, and found a strong correlation over the past four years.  This went all the way to the CEO, paired with passionate pleas for cross-merchandising before someone pointed out that every third fiscal month has an extra week in it (known as a “4,4,5” schedule), which was driving the correlation.

And more tactically, we’ve seen analytic PhDs apply for our positions without the fundamental skills of data processing and evaluations.  We are skeptical of analytic experts who don’t know SQL, and don’t have capabilities to describe large datasets.

Putting it back together:  The “last mile” in analytics is getting it into the hands of the people with their hands on the business levers.  While it sounds trivial, we have seen plenty of examples where the answer sat on a shelf or was simply disregarded because it failed to work in the context of the business.  This is obviously a hot button here at Penfield, as our core competence is “getting analytics done” which only ends with better decisions made through analytic insights.

 

 

 

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